Freedom Tech DC: The Case for the American Reserve Modernization Act with @RepNickBegich and @APompliano - ARMA would establish a permanent U.S. Bitcoin reserve with a 20-year hold - The bill would protect individuals’ right to hold their own keys - States could choose to use the federal custody framework - Consolidating government Bitcoin holdings and verifying reserves would make the reserve more transparent and accountable (00:00) — Introduction (00:47) — The reserve modernization proposal (02:21) — A 20-year hold and limits on sales (03:45) — Protecting individual self-custody (04:51) — Custody options for states (07:57) — Begich’s Mt. Gox experience (11:20) — Government holdings and reserve audits (13:20) — Bitcoin’s role in government reserves

The US holds $BTC today under an executive order. ARMA would mandate buying up to 200,000 more per year for five years, more than the network produces in that same period. At what point does sovereign accumulation change the nature of the asset itself?

Automated harness optimization methods keep improving, but which scenarios you train on is usually fixed from the start. This paper makes that "curriculum" itself adaptive. Title: ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization URL: 📝 Overview ActiveSaddler reframes which training scenarios feed agent harness optimization as an automated curriculum-learning problem, adapting that selection dynamically as optimization proceeds instead of fixing it upfront. ❗ Problem it solves Fixed scenario ordering abandons unresolved weaknesses too early or wastes rollouts on already-fixed issues. The value of revisiting known weaknesses versus exploring new ones shifts during optimization, but static schedules can't track that. 🛠️ Method Curriculum selection is modeled as a non-stationary bandit with dynamically instantiated arms — each arm a failure pattern, not an individual scenario. An LLM-based Arm Prioritizer scores learning potential every iteration across severity, fixability, breadth, and side-effect risk, while an Exploration Controller adaptively decides whether to exploit known weaknesses or explore unseen scenarios based on how many actionable targets remain. 📊 Results Pass@1 reached 59.8% on GAIA2 (+4.4 points over fixed random ordering) and 80.0% on Terminal-Bench 2.0 (+7.5 points). It's also far more cost-efficient — on GAIA2 it hits a target accuracy for $298 versus $1,360 for other methods. #AgentOptimization# #CurriculumLearning#

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